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Pytorch (MNIST)

Running and MNIST dataset classifier through CUSTOM image endpoints

Overview

This example shows a complete step in the pipeline through custom endpoints:
  1. 1.
    Pre-processing step
  2. 2.
    Serve a model step
  3. 3.
    Post-processing step

Adding package to the classpath

We need to add the main package to the classpath so that the notebook can load all the necessary libraries from Konduit-Serving into the Jupyter Notebook kernel.
%classpath add jar ../../konduit.jar
Classpaths can be considered similar to site-packages in the python ecosystem where each library that's to be imported to your code is loaded from.

Viewing the configuration file

To view the configuration file contents, use the following command and select the JSON file you want to view.
%%bash
less config.json
You'll be able to view the following.
{
"host" : "localhost",
"port" : 0,
"protocol" : "HTTP",
"pipeline" : {
"steps" : [ {
"@type" : "IMAGE_TO_NDARRAY",
"config" : {
"height" : 28,
"width" : 28,
"dataType" : "FLOAT",
"includeMinibatchDim" : true,
"aspectRatioHandling" : "CENTER_CROP",
"format" : "CHANNELS_FIRST",
"channelLayout" : "GRAYSCALE",
"normalization" : {
"type" : "SCALE"
},
"listHandling" : "NONE"
},
"keys" : [ "image" ],
"outputNames" : [ "Input3" ],
"keepOtherValues" : true,
"metadata" : false,
"metadataKey" : "@ImageToNDArrayStepMetadata"
}, {
"@type" : "LOGGING",
"logLevel" : "INFO",
"log" : "KEYS_AND_VALUES"
}, {
"@type" : "ONNX",
"modelUri" : "mnist.onnx",
"inputNames" : [ "Input3" ],
"outputNames" : [ "Plus214_Output_0" ]
}, {
"@type" : "CLASSIFIER_OUTPUT",
"inputName" : "Plus214_Output_0",
"labels" : [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9" ],
"allProbabilities" : false
} ]
}
}

Starting a server

Starts a server in the background with an id of onnx-mnist using config.json as configuration file without creating the manifest jar file before launching the server.
%%bash
konduit serve -id onnx-mnist -c config.json -rwm -b
You'll get the following message once the server starts in the background.
Starting konduit server...
Using classpath: /root/konduit/bin/../konduit.jar
INFO: Running command /root/miniconda/jre/bin/java -Dkonduit.logs.file.path=/root/.konduit-serving/command_logs/onnx-mnist.log -Dlogback.configurationFile=/tmp/logback-run_command_2ead2d4d1b15431d.xml -jar /root/konduit/bin/../konduit.jar run --instances 1 -s inference -c config.json -Dserving.id=onnx-mnist
For server status, execute: 'konduit list'
For logs, execute: 'konduit logs onnx-mnist'
We use konduit list command to view the list of activated server
%%bash
konduit list
The list of the activated server is below.
Listing konduit servers...
# | ID | TYPE | URL | PID | STATUS
1 | onnx-mnist | inference | localhost:33895 | 888 | started
To view the logs of the running server, use konduit logs commands.
%%bash
konduit logs onnx-mnist --lines 100
The output of server logging.
14:59:45.188 [main] INFO a.k.s.c.l.command.KonduitRunCommand - Processing configuration: /root/konduit/demos/2-pytorch-onnx-mnist/config.json
.
.
.
####################################################################
# #
# | / _ \ \ | _ \ | | _ _| __ __| | / | / #
# . < ( | . | | | | | | | . < . < #
# _|\_\ \___/ _|\_| ___/ \__/ ___| _| _|\_\ _) _|\_\ _) #
# #
####################################################################
14:59:45.601 [vert.x-worker-thread-0] INFO a.k.s.v.verticle.InferenceVerticle - Pending server start, please wait...
.
.
.
14:59:45.733 [vert.x-eventloop-thread-0] INFO a.k.s.v.p.h.v.InferenceVerticleHttp - Inference HTTP server is listening on host: 'localhost'
14:59:45.733 [vert.x-eventloop-thread-0] INFO a.k.s.v.p.h.v.InferenceVerticleHttp - Inference HTTP server started on port 33895 with 4 pipeline steps

Making a prediction

Let's display the test image before feeding it as an input into the model for the classification.
%%html
<img src="test-image.jpg" alt="title">
Here is the test image used in the prediction for this example:
konduit predict command is used to classify the image based on the model served in Konduit-Serving with the id given before.
%%bash
konduit predict onnx-mnist --input-type multipart '[email protected]'
You'll be able to get the output similar to the following.
{
"Plus214_Output_0" : [ [ -1.7924803, -9.652266, 11.478509, 5.148998, -7.9367347, 9.756878, 0.544513, -7.6820283, 9.234719, -6.431969 ] ],
"prob" : 11.478508949279785,
"index" : 2,
"label" : "2"
}

Stopping the server

After we're finished with the server, we can terminate it through the konduit stop command.
%%bash
konduit stop onnx-mnist
You'll receive this message once the server is terminated.
Stopping konduit server 'onnx-mnist'
Application 'onnx-mnist' terminated with status 0
Last modified 1yr ago